Globally Accessible Distributed Data Sharing (GADDS): a decentralized FAIR platform to facilitate data sharing in the life sciences

Abstract

Motivation Technical advances have revolutionized the life sciences and researchers commonly face challenges associated with handling large amounts of heterogeneous digital data. The Findable, Accessible, Interoperable and Reusable (FAIR) principles provide a framework to support effective data management. However, implementing this framework is beyond the means of most researchers in terms of resources and expertise, requiring awareness of metadata, policies, community agreements and other factors such as vocabularies and ontologies. Results We have developed the Globally Accessible Distributed Data Sharing (GADDS) platform to facilitate FAIR-like data-sharing in cross-disciplinary research collaborations. The platform consists of (i) a blockchain-based metadata quality control system, (ii) a private cloud-like storage system and (iii) a version control system. GADDS is built with containerized technologies, providing minimal hardware standards and easing scalability, and offers decentralized trust via transparency of metadata, facilitating data exchange and collaboration. As a use case, we provide an example implementation in engineered living material technology within the Hybrid Technology Hub at the University of Oslo. Availability and implementation Demo version available at https://github.com/pavelvazquez/GADDS. Supplementary information Supplementary data are available at Bioinformatics online.

IP for Sharing and Opening Government Data Based on Big Data

“…(3) Data desensitization and decryption technology Data pre-processing is required for sensitive, confidential or sensitive data in the process of GA sharing, and the desensitization and declassification process mainly adopts generalization, suppression, scrambling and lossy techniques to achieve protection by sensitive data, and at the same time, through machine learning, data association, knowledge base and other technologies, sensitivity analysis is performed on the data after multi-dimensional aggregation to achieve desensitization and desensitization of data datasets with multiple and and data security [5].…”

Exploring Architectural Solutions for Implementing the FAIR Principles in Big Data Environments

“…The third group encompasses FAIR-compliant big data architectures that are generic enough to fit the concept of an SRA. To the best of our knowledge, only GADDS [Vazquez et al 2022] can be included in this group. GADDS employs cloud storage and processing, keeping data in an object storage and metadata in a blockchain environment.…”

Pharmaceuticals, toxicity and antimicrobial resistance. Advancing human health and environmental risk assessment

“…Therefore, it is fundamental to create a global, fully accessible, and comprehensive raw database in the public domain under decentralized control. I think that applying modern public distributed ledger technology to circumvent the abovementioned critical limitations, for example through the creation of a blockchain-based ecotoxicity database, could be an interesting solution to a long overdue open science problem in the regulatory environmental risk assessment field (Brock et al, 2021;Martin et al, 2019;Mohammadipanah and Sajedi, 2021;Van Norman, 2016;Vazquez et al, 2022). This could be accompanied by advanced (meta)data preservation, while following FAIR principles (Wilkinson et al, 2016), which would include information on provenance, circumstances of the production, identifiers, integrity, authenticity, fixity and rights.…”